Utilizing tree storage structures in a dispersed storage network

ABSTRACT

A method for execution by a dispersed storage and task (DST) execution unit includes receiving a slice write request via a network that includes a data slice and extracting metadata from the data slice. The metadata is stored in a metadata storage tree in a first memory device of the DST execution unit and the data slice is stored in a slice storage tree in a second memory device of the DST execution unit based on tree utilization parameters.

CROSS-REFERENCE TO RELATED APPLICATIONS

The present U.S. Utility Patent Application claims priority pursuant to35 U.S.C. §119(e) to U.S. Provisional Application No. 62/287,145,entitled “VERIFYING INTEGRITY OF ENCODED DATA SLICES”, filed Jan. 26,2016, which is hereby incorporated herein by reference in its entiretyand made part of the present U.S. Utility Patent Application for allpurposes.

STATEMENT REGARDING FEDERALLY SPONSORED RESEARCH OR DEVELOPMENT

Not applicable.

INCORPORATION-BY-REFERENCE OF MATERIAL SUBMITTED ON A COMPACT DISC

Not applicable.

BACKGROUND OF THE INVENTION

Technical Field of the Invention

This invention relates generally to computer networks and moreparticularly to dispersing error encoded data.

Description of Related Art

Computing devices are known to communicate data, process data, and/orstore data. Such computing devices range from wireless smart phones,laptops, tablets, personal computers (PC), work stations, and video gamedevices, to data centers that support millions of web searches, stocktrades, or on-line purchases every day. In general, a computing deviceincludes a central processing unit (CPU), a memory system, userinput/output interfaces, peripheral device interfaces, and aninterconnecting bus structure.

As is further known, a computer may effectively extend its CPU by using“cloud computing” to perform one or more computing functions (e.g., aservice, an application, an algorithm, an arithmetic logic function,etc.) on behalf of the computer. Further, for large services,applications, and/or functions, cloud computing may be performed bymultiple cloud computing resources in a distributed manner to improvethe response time for completion of the service, application, and/orfunction. For example, Hadoop is an open source software framework thatsupports distributed applications enabling application execution bythousands of computers.

In addition to cloud computing, a computer may use “cloud storage” aspart of its memory system. As is known, cloud storage enables a user,via its computer, to store files, applications, etc. on an Internetstorage system. The Internet storage system may include a RAID(redundant array of independent disks) system and/or a dispersed storagesystem that uses an error correction scheme to encode data for storage.

BRIEF DESCRIPTION OF THE SEVERAL VIEWS OF THE DRAWING(S)

FIG. 1 is a schematic block diagram of an embodiment of a dispersed ordistributed storage network (DSN) in accordance with the presentinvention;

FIG. 2 is a schematic block diagram of an embodiment of a computing corein accordance with the present invention;

FIG. 3 is a schematic block diagram of an example of dispersed storageerror encoding of data in accordance with the present invention;

FIG. 4 is a schematic block diagram of a generic example of an errorencoding function in accordance with the present invention;

FIG. 5 is a schematic block diagram of a specific example of an errorencoding function in accordance with the present invention;

FIG. 6 is a schematic block diagram of an example of a slice name of anencoded data slice (EDS) in accordance with the present invention;

FIG. 7 is a schematic block diagram of an example of dispersed storageerror decoding of data in accordance with the present invention;

FIG. 8 is a schematic block diagram of a generic example of an errordecoding function in accordance with the present invention;

FIG. 9 is a schematic block diagram of an embodiment of a dispersed ordistributed storage network (DSN) in accordance with the presentinvention; and

FIG. 10 is a logic diagram of an example of a method of utilizing treestorage structures in accordance with the present invention.

DETAILED DESCRIPTION OF THE INVENTION

FIG. 1 is a schematic block diagram of an embodiment of a dispersed, ordistributed, storage network (DSN) 10 that includes a plurality ofcomputing devices 12-16, a managing unit 18, an integrity processingunit 20, and a DSN memory 22. The components of the DSN 10 are coupledto a network 24, which may include one or more wireless and/or wirelined communication systems; one or more non-public intranet systemsand/or public internet systems; and/or one or more local area networks(LAN) and/or wide area networks (WAN).

The DSN memory 22 includes a plurality of storage units 36 that may belocated at geographically different sites (e.g., one in Chicago, one inMilwaukee, etc.), at a common site, or a combination thereof. Forexample, if the DSN memory 22 includes eight storage units 36, eachstorage unit is located at a different site. As another example, if theDSN memory 22 includes eight storage units 36, all eight storage unitsare located at the same site. As yet another example, if the DSN memory22 includes eight storage units 36, a first pair of storage units are ata first common site, a second pair of storage units are at a secondcommon site, a third pair of storage units are at a third common site,and a fourth pair of storage units are at a fourth common site. Notethat a DSN memory 22 may include more or less than eight storage units36. Further note that each storage unit 36 includes a computing core (asshown in FIG. 2, or components thereof) and a plurality of memorydevices for storing dispersed error encoded data.

In various embodiments, each of the storage units operates as adistributed storage and task (DST) execution unit, and is operable tostore dispersed error encoded data and/or to execute, in a distributedmanner, one or more tasks on data. The tasks may be a simple function(e.g., a mathematical function, a logic function, an identify function,a find function, a search engine function, a replace function, etc.), acomplex function (e.g., compression, human and/or computer languagetranslation, text-to-voice conversion, voice-to-text conversion, etc.),multiple simple and/or complex functions, one or more algorithms, one ormore applications, etc. Hereafter, a storage unit may be interchangeablyreferred to as a dispersed storage and task (DST) execution unit and aset of storage units may be interchangeably referred to as a set of DSTexecution units.

Each of the computing devices 12-16, the managing unit 18, and theintegrity processing unit 20 include a computing core 26, which includesnetwork interfaces 30-33. Computing devices 12-16 may each be a portablecomputing device and/or a fixed computing device. A portable computingdevice may be a social networking device, a gaming device, a cell phone,a smart phone, a digital assistant, a digital music player, a digitalvideo player, a laptop computer, a handheld computer, a tablet, a videogame controller, and/or any other portable device that includes acomputing core. A fixed computing device may be a computer (PC), acomputer server, a cable set-top box, a satellite receiver, a televisionset, a printer, a fax machine, home entertainment equipment, a videogame console, and/or any type of home or office computing equipment.Note that each managing unit 18 and the integrity processing unit 20 maybe separate computing devices, may be a common computing device, and/ormay be integrated into one or more of the computing devices 12-16 and/orinto one or more of the storage units 36. In various embodiments,computing devices 12-16 can include user devices and/or can be utilizedby a requesting entity generating access requests, which can includerequests to read or write data to storage units in the DSN.

Each interface 30, 32, and 33 includes software and hardware to supportone or more communication links via the network 24 indirectly and/ordirectly. For example, interface 30 supports a communication link (e.g.,wired, wireless, direct, via a LAN, via the network 24, etc.) betweencomputing devices 14 and 16. As another example, interface 32 supportscommunication links (e.g., a wired connection, a wireless connection, aLAN connection, and/or any other type of connection to/from the network24) between computing devices 12 & 16 and the DSN memory 22. As yetanother example, interface 33 supports a communication link for each ofthe managing unit 18 and the integrity processing unit 20 to the network24.

Computing devices 12 and 16 include a dispersed storage (DS) clientmodule 34, which enables the computing device to dispersed storage errorencode and decode data as subsequently described with reference to oneor more of FIGS. 3-8. In this example embodiment, computing device 16functions as a dispersed storage processing agent for computing device14. In this role, computing device 16 dispersed storage error encodesand decodes data on behalf of computing device 14. With the use ofdispersed storage error encoding and decoding, the DSN 10 is tolerant ofa significant number of storage unit failures (the number of failures isbased on parameters of the dispersed storage error encoding function)without loss of data and without the need for a redundant or backupcopies of the data. Further, the DSN 10 stores data for an indefiniteperiod of time without data loss and in a secure manner (e.g., thesystem is very resistant to unauthorized attempts at accessing thedata).

In operation, the managing unit 18 performs DS management services. Forexample, the managing unit 18 establishes distributed data storageparameters (e.g., vault creation, distributed storage parameters,security parameters, billing information, user profile information,etc.) for computing devices 12-14 individually or as part of a group ofuser devices. As a specific example, the managing unit 18 coordinatescreation of a vault (e.g., a virtual memory block associated with aportion of an overall namespace of the DSN) within the DSN memory 22 fora user device, a group of devices, or for public access and establishesper vault dispersed storage (DS) error encoding parameters for a vault.The managing unit 18 facilitates storage of DS error encoding parametersfor each vault by updating registry information of the DSN 10, where theregistry information may be stored in the DSN memory 22, a computingdevice 12-16, the managing unit 18, and/or the integrity processing unit20.

The DSN managing unit 18 creates and stores user profile information(e.g., an access control list (ACL)) in local memory and/or withinmemory of the DSN memory 22. The user profile information includesauthentication information, permissions, and/or the security parameters.The security parameters may include encryption/decryption scheme, one ormore encryption keys, key generation scheme, and/or dataencoding/decoding scheme.

The DSN managing unit 18 creates billing information for a particularuser, a user group, a vault access, public vault access, etc. Forinstance, the DSN managing unit 18 tracks the number of times a useraccesses a non-public vault and/or public vaults, which can be used togenerate a per-access billing information. In another instance, the DSNmanaging unit 18 tracks the amount of data stored and/or retrieved by auser device and/or a user group, which can be used to generate aper-data-amount billing information.

As another example, the managing unit 18 performs network operations,network administration, and/or network maintenance. Network operationsincludes authenticating user data allocation requests (e.g., read and/orwrite requests), managing creation of vaults, establishingauthentication credentials for user devices, adding/deleting components(e.g., user devices, storage units, and/or computing devices with a DSclient module 34) to/from the DSN 10, and/or establishing authenticationcredentials for the storage units 36. Network administration includesmonitoring devices and/or units for failures, maintaining vaultinformation, determining device and/or unit activation status,determining device and/or unit loading, and/or determining any othersystem level operation that affects the performance level of the DSN 10.Network maintenance includes facilitating replacing, upgrading,repairing, and/or expanding a device and/or unit of the DSN 10.

The integrity processing unit 20 performs rebuilding of ‘bad’ or missingencoded data slices. At a high level, the integrity processing unit 20performs rebuilding by periodically attempting to retrieve/list encodeddata slices, and/or slice names of the encoded data slices, from the DSNmemory 22. For retrieved encoded slices, they are checked for errors dueto data corruption, outdated version, etc. If a slice includes an error,it is flagged as a ‘bad’ slice. For encoded data slices that were notreceived and/or not listed, they are flagged as missing slices. Badand/or missing slices are subsequently rebuilt using other retrievedencoded data slices that are deemed to be good slices to produce rebuiltslices. The rebuilt slices are stored in the DSN memory 22.

FIG. 2 is a schematic block diagram of an embodiment of a computing core26 that includes a processing module 50, a memory controller 52, mainmemory 54, a video graphics processing unit 55, an input/output (10)controller 56, a peripheral component interconnect (PCI) interface 58,an 10 interface module 60, at least one 10 device interface module 62, aread only memory (ROM) basic input output system (BIOS) 64, and one ormore memory interface modules. The one or more memory interfacemodule(s) includes one or more of a universal serial bus (USB) interfacemodule 66, a host bus adapter (HBA) interface module 68, a networkinterface module 70, a flash interface module 72, a hard drive interfacemodule 74, and a DSN interface module 76.

The DSN interface module 76 functions to mimic a conventional operatingsystem (OS) file system interface (e.g., network file system (NFS),flash file system (FFS), disk file system (DFS), file transfer protocol(FTP), web-based distributed authoring and versioning (WebDAV), etc.)and/or a block memory interface (e.g., small computer system interface(SCSI), interne small computer system interface (iSCSI), etc.). The DSNinterface module 76 and/or the network interface module 70 may functionas one or more of the interface 30-33 of FIG. 1. Note that the 10 deviceinterface module 62 and/or the memory interface modules 66-76 may becollectively or individually referred to as 10 ports.

FIG. 3 is a schematic block diagram of an example of dispersed storageerror encoding of data. When a computing device 12 or 16 has data tostore it disperse storage error encodes the data in accordance with adispersed storage error encoding process based on dispersed storageerror encoding parameters. Here, the computing device stores data object40, which can include a file (e.g., text, video, audio, etc.), or otherdata arrangement. The dispersed storage error encoding parametersinclude an encoding function (e.g., information dispersal algorithm(IDA), Reed-Solomon, Cauchy Reed-Solomon, systematic encoding,non-systematic encoding, on-line codes, etc.), a data segmentingprotocol (e.g., data segment size, fixed, variable, etc.), and per datasegment encoding values. The per data segment encoding values include atotal, or pillar width, number (T) of encoded data slices per encodingof a data segment i.e., in a set of encoded data slices); a decodethreshold number (D) of encoded data slices of a set of encoded dataslices that are needed to recover the data segment; a read thresholdnumber (R)of encoded data slices to indicate a number of encoded dataslices per set to be read from storage for decoding of the data segment;and/or a write threshold number (W) to indicate a number of encoded dataslices per set that must be accurately stored before the encoded datasegment is deemed to have been properly stored. The dispersed storageerror encoding parameters may further include slicing information (e.g.,the number of encoded data slices that will be created for each datasegment) and/or slice security information (e.g., per encoded data sliceencryption, compression, integrity checksum, etc.).

In the present example, Cauchy Reed-Solomon has been selected as theencoding function (a generic example is shown in FIG. 4 and a specificexample is shown in FIG. 5); the data segmenting protocol is to dividethe data object into fixed sized data segments; and the per data segmentencoding values include: a pillar width of 5, a decode threshold of 3, aread threshold of 4, and a write threshold of 4. In accordance with thedata segmenting protocol, the computing device 12 or 16 divides dataobject 40 into a plurality of fixed sized data segments (e.g., 1 throughY of a fixed size in range of Kilo-bytes to Tera-bytes or more). Thenumber of data segments created is dependent of the size of the data andthe data segmenting protocol.

The computing device 12 or 16 then disperse storage error encodes a datasegment using the selected encoding function (e.g., Cauchy Reed-Solomon)to produce a set of encoded data slices. FIG. 4 illustrates a genericCauchy Reed-Solomon encoding function, which includes an encoding matrix(EM), a data matrix (DM), and a coded matrix (CM). The size of theencoding matrix (EM) is dependent on the pillar width number (T) and thedecode threshold number (D) of selected per data segment encodingvalues. To produce the data matrix (DM), the data segment is dividedinto a plurality of data blocks and the data blocks are arranged into Dnumber of rows with Z data blocks per row. Note that Z is a function ofthe number of data blocks created from the data segment and the decodethreshold number (D). The coded matrix is produced by matrix multiplyingthe data matrix by the encoding matrix.

FIG. 5 illustrates a specific example of Cauchy Reed-Solomon encodingwith a pillar number (T) of five and decode threshold number of three.In this example, a first data segment is divided into twelve data blocks(D1-D12). The coded matrix includes five rows of coded data blocks,where the first row of X11-X14 corresponds to a first encoded data slice(EDS 1_1), the second row of X21-X24 corresponds to a second encodeddata slice (EDS 2_1), the third row of X31-X34 corresponds to a thirdencoded data slice (EDS 3_1), the fourth row of X41-X44 corresponds to afourth encoded data slice (EDS 4_1), and the fifth row of X51-X54corresponds to a fifth encoded data slice (EDS 5_1). Note that thesecond number of the EDS designation corresponds to the data segmentnumber.

Returning to the discussion of FIG. 3, the computing device also createsa slice name (SN) for each encoded data slice (EDS) in the set ofencoded data slices. A typical format for a slice name 80 is shown inFIG. 6. As shown, the slice name (SN) 80 includes a pillar number of theencoded data slice (e.g., one of 1-T), a data segment number (e.g., oneof 1-Y), a vault identifier (ID), a data object identifier (ID), and mayfurther include revision level information of the encoded data slices.The slice name functions as, at least part of, a DSN address for theencoded data slice for storage and retrieval from the DSN memory 22.

As a result of encoding, the computing device 12 or 16 produces aplurality of sets of encoded data slices, which are provided with theirrespective slice names to the storage units for storage. As shown, thefirst set of encoded data slices includes EDS 1_1 through EDS 5_1 andthe first set of slice names includes SN 1_1 through SN 5_1 and the lastset of encoded data slices includes EDS 1_Y through EDS 5_Y and the lastset of slice names includes SN 1_Y through SN 5_Y.

FIG. 7 is a schematic block diagram of an example of dispersed storageerror decoding of a data object that was dispersed storage error encodedand stored in the example of FIG. 4. In this example, the computingdevice 12 or 16 retrieves from the storage units at least the decodethreshold number of encoded data slices per data segment. As a specificexample, the computing device retrieves a read threshold number ofencoded data slices.

To recover a data segment from a decode threshold number of encoded dataslices, the computing device uses a decoding function as shown in FIG.8. As shown, the decoding function is essentially an inverse of theencoding function of FIG. 4. The coded matrix includes a decodethreshold number of rows (e.g., three in this example) and the decodingmatrix in an inversion of the encoding matrix that includes thecorresponding rows of the coded matrix. For example, if the coded matrixincludes rows 1, 2, and 4, the encoding matrix is reduced to rows 1, 2,and 4, and then inverted to produce the decoding matrix.

FIG. 9 is a schematic block diagram of another embodiment of a dispersedstorage network (DSN) that includes the distributed storage and task(DST) processing unit 916, the network 24 of FIG. 1, and a set of DSTexecution units 1-n . The DST processing unit can be implemented byutilizing the computing device 16 of FIG. 1, for example, functioning asa dispersed storage processing agent for computing device 14 asdescribed previously. Each DST execution unit can be implemented byutilizing the storage unit 36 of FIG. 1, for example, operable to storedispersed error encoded data and/or to execute, in a distributed manner,one or more tasks on data as described previously. Each DST executionunit can include a processing module 84, a solid-state memory 986, and amagnetic drive memory 988, which can be implemented, for example, byutilizing the computing core of FIG. 2. The DSN functions to store slicemetadata and encoded data slices.

In an example of operation of the storing of the slice metadata and theencoded data slices, when initializing utilization of the storage unit,the processing module 84 can establish a storage tree structure withinone or more memories, where the one or more memories includes at leasttwo memory types (e.g., the solid-state memory 986 and the magneticdrive memory 988), where a top of the tree structure includes storagetrees to store slice metadata and a bottom of the tree structureincludes storage trees to store encoded data slices associated with theslice metadata. Establishing the storage tree structure can includeidentifying available memory types, generating tree structures, and/orstoring tree structures within the memory types.

Having established the storage tree structure, when receiving a sliceaccess transmission that includes a write slice request, the processingmodule 84 can utilize the tree structure to accommodate storage of oneor more encoded data slices of the write slice request in accordancewith a tree utilization approach. Tree utilization can includesextracting slice metadata from the message, storing a slice metadata ina storage tree associated with a higher performance memory (e.g., thesolid-state memory 986) when the tree utilization approach includesstoring metadata in the higher performance memory, and/or storing theencoded data slices at a location associated with the slice metadata ina tree structure that is associated with the lower performance memorywhen the tree utilization approach includes storing encoded data slicesin the lower performance memory (e.g., in the magnetic drive memory988).

When utilization of a tree within the tree structure comparesunfavorably to a tree utilization threshold level (e.g., exceeds thethreshold level and/or too many entries beyond the capability of aphysical memory), the processing module 84 can expand the tree structureto accommodate further storage capability. For example, the processingmodule 84 adds another tree to the tree structure and associates theother tree with a best available memory type.

In various embodiments, a processing system of a dispersed storage andtask (DST) execution unit includes at least one processor and a memorythat stores operational instructions, that when executed by the at leastone processor cause the processing system to receive a slice writerequest via a network that includes a data slice and extract metadatafrom the data slice. The metadata is stored in a metadata storage treein a first memory device of the DST execution unit and the data slice isstored in a slice storage tree in a second memory device of the DSTexecution unit based on tree utilization parameters.

In various embodiments, the first memory device has a higher performancelevel than the second memory device based on the tree utilizationparameters indicating that metadata is stored in higher performancememory than data slices. In various embodiments, the first memory devicehas a lower performance level than the second memory device based on thetree utilization parameters indicating that metadata is stored in lowerperformance memory than data slices. In various embodiments, the firstmemory device includes solid-state memory, and wherein the second memorydevice includes magnetic drive memory. In various embodiments, metadataentries of the metadata storage tree include a tree addresscorresponding to a location of the corresponding data slice in the slicestorage tree.

In various embodiments, the tree utilization parameters are generated byselecting the first memory device to store the metadata storage tree andselecting the second memory device to store the slice storage tree. Aninitial metadata storage tree structure is generated in the first memoryand an initial slice storage tree structure is generated in the secondmemory. In various embodiments, generating the tree utilizationparameters further includes selecting a first plurality of memorydevices to store a plurality of metadata storage trees and selecting asecond plurality of memory devices to store a plurality of slice storagetrees. In various embodiments, the first memory device is selected fromthe first plurality of memory devices to store the extracted metadataand the second memory device is selected from the second plurality ofmemory devices to store the received data slice based on the treeutilization parameters.

In various embodiments, a tree utilization threshold is compared to ametadata tree utilization level associated with the metadata storagetree and/or a slice tree utilization level associated with the slicestorage tree. The tree utilization parameters are updated by selecting athird memory device to store a third storage tree when tree utilizationthreshold compares unfavorably to the metadata tree utilization leveland/or the slice tree utilization level. In various embodiments, thethird memory device includes a highest performing memory type, and thethird memory device is selected based on the highest performing memorytype.

FIG. 10 is a flowchart illustrating an example of utilizing tree storagestructures. In particular, a method is presented for use in associationwith one or more functions and features described in conjunction withFIGS. 1-9, for execution by a dispersed storage and task (DST) executionunit that includes a processor or via another processing system of adispersed storage network that includes at least one processor andmemory that stores instruction that configure the processor orprocessors to perform the steps described below. Step 1002 includesreceiving a slice write request via a network that includes a dataslice. Step 1004 includes extracting metadata from the data slice. Step1006 includes storing the metadata in a metadata storage tree in a firstmemory device of the DST execution unit and storing the data slice in aslice storage tree in a second memory device of the DST execution unitbased on tree utilization parameters.

In various embodiments, the first memory device has a higher performancelevel than the second memory device based on the tree utilizationparameters indicating that metadata is stored in higher performancememory than data slices. In various embodiments, the first memory devicehas a lower performance level than the second memory device based on thetree utilization parameters indicating that metadata is stored in lowerperformance memory than data slices. In various embodiments, the firstmemory device includes solid-state memory, and wherein the second memorydevice includes magnetic drive memory. In various embodiments, metadataentries of the metadata storage tree include a tree addresscorresponding to a location of the corresponding data slice in the slicestorage tree.

In various embodiments, the tree utilization parameters are generated byselecting the first memory device to store the metadata storage tree andselecting the second memory device to store the slice storage tree. Aninitial metadata storage tree structure is generated in the first memoryand an initial slice storage tree structure is generated in the secondmemory. In various embodiments, generating the tree utilizationparameters further includes selecting a first plurality of memorydevices to store a plurality of metadata storage trees and selecting asecond plurality of memory devices to store a plurality of slice storagetrees. In various embodiments, the first memory device is selected fromthe first plurality of memory devices to store the extracted metadataand the second memory device is selected from the second plurality ofmemory devices to store the received data slice based on the treeutilization parameters.

In various embodiments, a tree utilization threshold is compared to ametadata tree utilization level associated with the metadata storagetree and/or a slice tree utilization level associated with the slicestorage tree. The tree utilization parameters are updated by selecting athird memory device to store a third storage tree when tree utilizationthreshold compares unfavorably to the metadata tree utilization leveland/or the slice tree utilization level. In various embodiments, thethird memory device includes a highest performing memory type, and thethird memory device is selected based on the highest performing memorytype.

In various embodiments, a non-transitory computer readable storagemedium includes at least one memory section that stores operationalinstructions that, when executed by a processing system of a dispersedstorage network (DSN) that includes a processor and a memory, causes theprocessing system to receive a slice write request via a network thatincludes a data slice and extract metadata from the data slice. Themetadata is stored in a metadata storage tree in a first memory deviceof the DST execution unit and the data slice is stored in a slicestorage tree in a second memory device of the DST execution unit basedon tree utilization parameters.

It is noted that terminologies as may be used herein such as bit stream,stream, signal sequence, etc. (or their equivalents) have been usedinterchangeably to describe digital information whose contentcorresponds to any of a number of desired types (e.g., data, video,speech, audio, etc. any of which may generally be referred to as‘data’).

As may be used herein, the terms “substantially” and “approximately”provides an industry-accepted tolerance for its corresponding termand/or relativity between items. Such an industry-accepted toleranceranges from less than one percent to fifty percent and corresponds to,but is not limited to, component values, integrated circuit processvariations, temperature variations, rise and fall times, and/or thermalnoise. Such relativity between items ranges from a difference of a fewpercent to magnitude differences. As may also be used herein, theterm(s) “configured to”, “operably coupled to”, “coupled to”, and/or“coupling” includes direct coupling between items and/or indirectcoupling between items via an intervening item (e.g., an item includes,but is not limited to, a component, an element, a circuit, and/or amodule) where, for an example of indirect coupling, the intervening itemdoes not modify the information of a signal but may adjust its currentlevel, voltage level, and/or power level. As may further be used herein,inferred coupling (i.e., where one element is coupled to another elementby inference) includes direct and indirect coupling between two items inthe same manner as “coupled to”. As may even further be used herein, theterm “configured to”, “operable to”, “coupled to”, or “operably coupledto” indicates that an item includes one or more of power connections,input(s), output(s), etc., to perform, when activated, one or more itscorresponding functions and may further include inferred coupling to oneor more other items. As may still further be used herein, the term“associated with”, includes direct and/or indirect coupling of separateitems and/or one item being embedded within another item.

As may be used herein, the term “compares favorably”, indicates that acomparison between two or more items, signals, etc., provides a desiredrelationship. For example, when the desired relationship is that signal1 has a greater magnitude than signal 2, a favorable comparison may beachieved when the magnitude of signal 1 is greater than that of signal 2or when the magnitude of signal 2 is less than that of signal 1. As maybe used herein, the term “compares unfavorably”, indicates that acomparison between two or more items, signals, etc., fails to providethe desired relationship.

As may also be used herein, the terms “processing module”, “processingcircuit”, “processor”, and/or “processing unit” may be a singleprocessing device or a plurality of processing devices. Such aprocessing device may be a microprocessor, micro-controller, digitalsignal processor, microcomputer, central processing unit, fieldprogrammable gate array, programmable logic device, state machine, logiccircuitry, analog circuitry, digital circuitry, and/or any device thatmanipulates signals (analog and/or digital) based on hard coding of thecircuitry and/or operational instructions. The processing module,module, processing circuit, and/or processing unit may be, or furtherinclude, memory and/or an integrated memory element, which may be asingle memory device, a plurality of memory devices, and/or embeddedcircuitry of another processing module, module, processing circuit,and/or processing unit. Such a memory device may be a read-only memory,random access memory, volatile memory, non-volatile memory, staticmemory, dynamic memory, flash memory, cache memory, and/or any devicethat stores digital information. Note that if the processing module,module, processing circuit, and/or processing unit includes more thanone processing device, the processing devices may be centrally located(e.g., directly coupled together via a wired and/or wireless busstructure) or may be distributedly located (e.g., cloud computing viaindirect coupling via a local area network and/or a wide area network).Further note that if the processing module, module, processing circuit,and/or processing unit implements one or more of its functions via astate machine, analog circuitry, digital circuitry, and/or logiccircuitry, the memory and/or memory element storing the correspondingoperational instructions may be embedded within, or external to, thecircuitry comprising the state machine, analog circuitry, digitalcircuitry, and/or logic circuitry. Still further note that, the memoryelement may store, and the processing module, module, processingcircuit, and/or processing unit executes, hard coded and/or operationalinstructions corresponding to at least some of the steps and/orfunctions illustrated in one or more of the Figures. Such a memorydevice or memory element can be included in an article of manufacture.

One or more embodiments have been described above with the aid of methodsteps illustrating the performance of specified functions andrelationships thereof. The boundaries and sequence of these functionalbuilding blocks and method steps have been arbitrarily defined hereinfor convenience of description. Alternate boundaries and sequences canbe defined so long as the specified functions and relationships areappropriately performed. Any such alternate boundaries or sequences arethus within the scope and spirit of the claims. Further, the boundariesof these functional building blocks have been arbitrarily defined forconvenience of description. Alternate boundaries could be defined aslong as the certain significant functions are appropriately performed.Similarly, flow diagram blocks may also have been arbitrarily definedherein to illustrate certain significant functionality.

To the extent used, the flow diagram block boundaries and sequence couldhave been defined otherwise and still perform the certain significantfunctionality. Such alternate definitions of both functional buildingblocks and flow diagram blocks and sequences are thus within the scopeand spirit of the claims. One of average skill in the art will alsorecognize that the functional building blocks, and other illustrativeblocks, modules and components herein, can be implemented as illustratedor by discrete components, application specific integrated circuits,processors executing appropriate software and the like or anycombination thereof.

In addition, a flow diagram may include a “start” and/or “continue”indication. The “start” and “continue” indications reflect that thesteps presented can optionally be incorporated in or otherwise used inconjunction with other routines. In this context, “start” indicates thebeginning of the first step presented and may be preceded by otheractivities not specifically shown. Further, the “continue” indicationreflects that the steps presented may be performed multiple times and/ormay be succeeded by other activities not specifically shown. Further,while a flow diagram indicates a particular ordering of steps, otherorderings are likewise possible provided that the principles ofcausality are maintained.

The one or more embodiments are used herein to illustrate one or moreaspects, one or more features, one or more concepts, and/or one or moreexamples. A physical embodiment of an apparatus, an article ofmanufacture, a machine, and/or of a process may include one or more ofthe aspects, features, concepts, examples, etc. described with referenceto one or more of the embodiments discussed herein. Further, from figureto figure, the embodiments may incorporate the same or similarly namedfunctions, steps, modules, etc. that may use the same or differentreference numbers and, as such, the functions, steps, modules, etc. maybe the same or similar functions, steps, modules, etc. or differentones.

Unless specifically stated to the contra, signals to, from, and/orbetween elements in a figure of any of the figures presented herein maybe analog or digital, continuous time or discrete time, and single-endedor differential. For instance, if a signal path is shown as asingle-ended path, it also represents a differential signal path.Similarly, if a signal path is shown as a differential path, it alsorepresents a single-ended signal path. While one or more particulararchitectures are described herein, other architectures can likewise beimplemented that use one or more data buses not expressly shown, directconnectivity between elements, and/or indirect coupling between otherelements as recognized by one of average skill in the art.

The term “module” is used in the description of one or more of theembodiments. A module implements one or more functions via a device suchas a processor or other processing device or other hardware that mayinclude or operate in association with a memory that stores operationalinstructions. A module may operate independently and/or in conjunctionwith software and/or firmware. As also used herein, a module may containone or more sub-modules, each of which may be one or more modules.

As may further be used herein, a computer readable memory includes oneor more memory elements. A memory element may be a separate memorydevice, multiple memory devices, or a set of memory locations within amemory device. Such a memory device may be a read-only memory, randomaccess memory, volatile memory, non-volatile memory, static memory,dynamic memory, flash memory, cache memory, and/or any device thatstores digital information. The memory device may be in a form a solidstate memory, a hard drive memory, cloud memory, thumb drive, servermemory, computing device memory, and/or other physical medium forstoring digital information.

While particular combinations of various functions and features of theone or more embodiments have been expressly described herein, othercombinations of these features and functions are likewise possible. Thepresent disclosure is not limited by the particular examples disclosedherein and expressly incorporates these other combinations.

What is claimed is:
 1. A method for execution by a dispersed storage andtask (DST) execution unit that includes a processor, the methodcomprises: receiving a slice write request via a network that includes adata slice; extracting metadata from the data slice; and storing themetadata in a metadata storage tree in a first memory device of the DSTexecution unit and storing the data slice in a slice storage tree in asecond memory device of the DST execution unit based on tree utilizationparameters.
 2. The method of claim 1, wherein the first memory devicehas a higher performance level than the second memory device based onthe tree utilization parameters indicating that metadata is stored inhigher performance memory than data slices.
 3. The method of claim 1,wherein the first memory device has a lower performance level than thesecond memory device based on the tree utilization parameters indicatingthat metadata is stored in lower performance memory than data slices. 4.The method of claim 1, wherein the first memory device includessolid-state memory, and wherein the second memory device includesmagnetic drive memory.
 5. The method of claim 1, further comprising:generating the tree utilization parameters by selecting the first memorydevice to store the metadata storage tree and selecting the secondmemory device to store the slice storage tree; generating an initialmetadata storage tree structure in the first memory; and generating aninitial slice storage tree structure in the second memory.
 6. The methodof claim 5, wherein generating the tree utilization parameters furtherincludes selecting a first plurality of memory devices to store aplurality of metadata storage trees and selecting a second plurality ofmemory devices to store a plurality of slice storage trees.
 7. Themethod of claim 6, further comprising selecting the first memory devicefrom the first plurality of memory devices to store the extractedmetadata and selecting the second memory device from the secondplurality of memory devices to store the received data slice based onthe tree utilization parameters.
 8. The method of claim 1, furthercomprising: comparing a tree utilization threshold to at least one of: ametadata tree utilization level associated with the metadata storagetree or a slice tree utilization level associated with the slice storagetree; and updating the tree utilization parameters by selecting a thirdmemory device to store a third storage tree when tree utilizationthreshold compares unfavorably to the at least one of: the metadata treeutilization level or the slice tree utilization level.
 9. The method ofclaim 8, wherein the third memory device includes a highest performingmemory type, and wherein the third memory device is selected based onthe highest performing memory type.
 10. The method of claim 1, whereinmetadata entries of the metadata storage tree include a tree addresscorresponding to a location of the corresponding data slice in the slicestorage tree.
 11. A processing system of a dispersed storage and task(DST) execution unit comprises: at least one processor; a memory thatstores operational instructions, that when executed by the at least oneprocessor cause the processing system to: receive a slice write requestvia a network that includes a data slice; extract metadata from the dataslice; and store the metadata in a metadata storage tree in a firstmemory device of the DST execution unit and store the data slice in aslice storage tree in a second memory device of the DST execution unitbased on tree utilization parameters.
 12. The processing system of claim11, wherein the first memory device has a higher performance level thanthe second memory device based on the tree utilization parametersindicating that metadata is stored in higher performance memory thandata slices.
 13. The processing system of claim 11, wherein the firstmemory device has a lower performance level than the second memorydevice based on the tree utilization parameters indicating that metadatais stored in lower performance memory than data slices.
 14. Theprocessing system of claim 11, wherein the first memory device includessolid-state memory, and wherein the second memory device includesmagnetic drive memory.
 15. The processing system of claim 11, whereinthe operational instructions, when executed by the at least oneprocessor, further cause the processing system to: generate the treeutilization parameters by selecting the first memory device to store themetadata storage tree and selecting the second memory device to storethe slice storage tree; generate an initial metadata storage treestructure in the first memory; and generate an initial slice storagetree structure in the second memory.
 16. The processing system of claim15, wherein generating the tree utilization parameters further includesselecting a first plurality of memory devices to store a plurality ofmetadata storage trees and selecting a second plurality of memorydevices to store a plurality of slice storage trees.
 17. The processingsystem of claim 16, further comprising selecting the first memory devicefrom the first plurality of memory devices to store the extractedmetadata and selecting the second memory device from the secondplurality of memory devices to store the received data slice based onthe tree utilization parameters.
 18. The processing system of claim 11,wherein the operational instructions, when executed by the at least oneprocessor, further cause the processing system to: compare a treeutilization threshold to at least one of: a metadata tree utilizationlevel associated with the metadata storage tree or a slice treeutilization level associated with the slice storage tree; and update thetree utilization parameters by selecting a third memory device to storea third storage tree when tree utilization threshold comparesunfavorably to the at least one of: the metadata tree utilization levelor the slice tree utilization level.
 19. The processing system of claim18, wherein the third memory device includes a highest performing memorytype, and wherein the third memory device is selected based on thehighest performing memory type.
 20. A non-transitory computer readablestorage medium comprises: at least one memory section that storesoperational instructions that, when executed by a processing system of adispersed storage network (DSN) that includes a processor and a memory,causes the processing system to: receive a slice write request via anetwork that includes a data slice; extract metadata from the dataslice; and store the metadata in a metadata storage tree in a firstmemory device and store the data slice in a slice storage tree in asecond memory device based on tree utilization parameters.